How Project Leaders Can Overcome the Crisis of Silence
Bibliographic record
Abstract
It is estimated that of the $255 billion spent per year on information technology projects in the United States, more than a quarter is burned up in failures and cost overruns. Project professionals and management experts have attempted to respond to these failures by improving the formal systems related to program governance, project management and project-related technologies. Though these new approaches have produced improved results, with more than two out of three projects continuing to disappoint, however, the authors argue that something is still missing. They suggest five crucial questions to ask to help prevent project failure ? are we planning around facts, is the project sponsor providing support, are we faithful to the process, are we honestly assessing our progress and risk and are team members pulling their weight. The authors examine the success of the project managers who do engage in these conversations and then lay out a plan for using them in the organization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".